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Least Squares and Maximum Likelihood Estimation of Mixed Spectra

Brynolfsson, Johan LU ; Swärd, Johan LU ; Jakobsson, Andreas LU and Sandsten, Maria LU (2018) p.2345-2349
Abstract
In this paper, we propose a novel 1-D spectral
estimator for signals with mixed spectra. The proposed method
is partly based on the recently introduced smooth spectral
estimator LIMES, in which the smoothness is accounted for by
assuming linearity within predefined segments of the spectrum.
The proposed method utilizes this formulation but also allows
segments to change size to better estimate the spectrum, thereby
allowing for the estimation of spectra that are neither completely
smooth or sparse in frequency, but rather contains a mixture
of such components. Using simulated data, we illustrate the
performance of the proposed estimator, comparing to other recent
spectral estimation techniques.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
26th European Signal Processing Conference, EUSIPCO 2018.
pages
2345 - 2349
publisher
European Signal Processing Conference, EUSIPCO
external identifiers
  • scopus:85059811194
ISBN
978-908279701-5
DOI
10.23919/EUSIPCO.2018.8553105
language
English
LU publication?
yes
id
aed250bf-da22-42d6-9dd1-15b1e87b5086
date added to LUP
2018-09-26 08:26:55
date last changed
2019-03-08 02:34:12
@inproceedings{aed250bf-da22-42d6-9dd1-15b1e87b5086,
  abstract     = {In this paper, we propose a novel 1-D spectral<br/>estimator for signals with mixed spectra. The proposed method<br/>is partly based on the recently introduced smooth spectral<br/>estimator LIMES, in which the smoothness is accounted for by<br/>assuming linearity within predefined segments of the spectrum.<br/>The proposed method utilizes this formulation but also allows<br/>segments to change size to better estimate the spectrum, thereby<br/>allowing for the estimation of spectra that are neither completely<br/>smooth or sparse in frequency, but rather contains a mixture<br/>of such components. Using simulated data, we illustrate the<br/>performance of the proposed estimator, comparing to other recent<br/>spectral estimation techniques.},
  author       = {Brynolfsson, Johan and Swärd, Johan and Jakobsson, Andreas and Sandsten, Maria},
  isbn         = {978-908279701-5},
  language     = {eng},
  pages        = {2345--2349},
  publisher    = {European Signal Processing Conference, EUSIPCO},
  title        = {Least Squares and Maximum Likelihood Estimation of Mixed Spectra},
  url          = {http://dx.doi.org/10.23919/EUSIPCO.2018.8553105},
  year         = {2018},
}